This study demonstrates that generative large language models can be utilized in a more flexible manner for DNA sequence analysis and classification tasks compared to traditional transformer encoder-based models. While recent encoder-based models such as DNABERT and Nucleotide Transformer have shown significant performance in DNA sequence classification, transformer decoder-based generative models have not yet been extensively explored in this field. This study evaluates how effectively generative Large Language Models handle DNA sequences with various labels and analyzes performance changes when additional textual information is provided. Experiments were conducted on antimicrobial resistance genes, and the results show that generative Large Language Models can offer comparable or potentially better predictions, demonstrating flexibility and accuracy when incorporating both sequence and textual information. The code and data used in this work are available at the following GitHub repository: https://github.com/biocomgit/llm4dna.